Matching Items (5)
Filtering by
- All Subjects: Executive Summary
- All Subjects: Law
- Member of: Theses and Dissertations
- Status: Published
Description
E-commerce has rapidly become a mainstay in today's economy, and many websites have built themselves around providing a platform for independent sellers. Sites such as Etsy, Storenvy, Redbubble, and Society6 are increasingly popular options for anyone looking to open their own online store. With this project, I attempted to examine the effects of four different marketing techniques on sales in an online store. I opened a shop on Etsy and tracked sales in connection with promotion through social media, selling products in-person at a convention, holding a holiday tie-in sale, and using price anchoring. Social media accounts were opened on Facebook, Tumblr, and Instagram to promote the shop over the course of the project period, and Etsy's web analytics were used to track which sites directed the most traffic to the shop. I attended a convention in mid-January 2016 where I sold my products and distributed business cards with a discount code to track sales resulting from being at the convention. A holiday sale was held in conjunction with Valentine's Day to look at whether holidays influenced purchases. Lastly, a significantly more expensive product was temporarily put in the shop to see whether it produced a price anchoring effect \u2014 that is, encouraged sales of the less expensive products by making them seem affordable in comparison. While the volume of sales data was too small to draw statistically significant conclusions, the project was a highly instructive experience in the process of opening a small online store. The decision-making steps outlined may be helpful to other students looking to open their own online shop.
ContributorsChen, Candice Elizabeth (Author) / Moore, James (Thesis director) / Sanford, Adriana (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
New Venture Group, a student-run consulting organization at ASU, collaborated with representatives from Intel Corporation to determine current best supplier management practices in the area of capital equipment procurement. The New Venture Group team accomplished this goal by completing the following deliverables: (1) Research and consolidate best practices for managing capital equipment suppliers. (2) Interview suppliers of capital equipment in the semiconductor industry to understand their motivators. (3) Examine top supply chain companies that utilize capital equipment manufacturers within their procurement systems. (4) Gather data and knowledge in conjunction with Intel Corporation's current practices to improve the effectiveness of the company's supplier management techniques regarding capital equipment manufacturers. The thesis report outlines the key insights and recommendations that our team extracted from the research that we performed. Our team analyzed peer-reviewed journal articles, conducted interviews with suppliers of capital equipment to semiconductor manufacturers, and surveyed buyers at top companies to reach important key insights. We then used these insights to develop the following strategies to improve Intel's capital equipment supplier management structure: All Suppliers 1. Allow high-performance suppliers to select one reward from an established portfolio of incentives. 2. Increase measurement frequency for specific metrics. 3. Use collaborative two-way measurement with a corresponding balanced scorecard. Key Suppliers of Critical Products 4. Conduct gap analysis through supplier self-assessments. 5. Implement collaborative target pricing. 6. Delegate an Ombudsman. 7. Create a value map to determine the strengths and incentivize collaboration. 8. Create comparison charts comparing supplier technological competencies versus Intel's product developments. 9. Establish a systematized product development process and strategic sourcing strategy that supports the continuation of Moore's Law.
ContributorsSantiago, Bryce (Co-author) / Chen, Jenny (Co-author) / Chang, Karen (Co-author) / Baldridge, Stephen (Co-author) / Laub, Jeffrey (Thesis director) / Brooks, Daniel (Committee member) / Department of Information Systems (Contributor, Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Only an Executive Summary of the project is included.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
The goal of this project is to develop a deeper understanding of how machine learning pertains to the business world and how business professionals can capitalize on its capabilities. It explores the end-to-end process of integrating a machine and the tradeoffs and obstacles to consider. This topic is extremely pertinent today as the advent of big data increases and the use of machine learning and artificial intelligence is expanding across industries and functional roles. The approach I took was to expand on a project I championed as a Microsoft intern where I facilitated the integration of a forecasting machine learning model firsthand into the business. I supplement my findings from the experience with research on machine learning as a disruptive technology. This paper will not delve into the technical aspects of coding a machine model, but rather provide a holistic overview of developing the model from a business perspective. My findings show that, while the advantages of machine learning are large and widespread, a lack of visibility and transparency into the algorithms behind machine learning, the necessity for large amounts of data, and the overall complexity of creating accurate models are all tradeoffs to consider when deciding whether or not machine learning is suitable for a certain objective. The results of this paper are important in order to increase the understanding of any business professional on the capabilities and obstacles of integrating machine learning into their business operations.
ContributorsVerma, Ria (Author) / Goegan, Brian (Thesis director) / Moore, James (Committee member) / Department of Information Systems (Contributor) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
After the landmark case, Gideon v Wainwright was heard by the United States Supreme Court in 1963, the 6th Amendment granted counsel to indigent defendants. However, since 1963 the United States population has skyrocketed and so have arrest rates leaving many public defenders underpaid and overworked. Knowing these facts Can Justice be Bought uses interviews, real-life stories, and research to determine if the 6th Amendment is upheld in the way the system is currently working, and are indigent defendants given a fair chance at trial. After an overview of public defense in the United States as a whole, it becomes clear that in many states the way the system is operating gives them less than a fair chance at justice. This, however, is not from a lack of effort from public defenders, they are simply just so overworked by exorbitant caseloads that they cannot possibly give each of their cases the time it deserves. However, not all indigent defense systems were created equal, states like Maryland have a number of resources for their public defenders that set them up for success. In order to close the gap between private counsel and public defense in the United States, public defenders’ offices should begin to allocate more funding in order to lighten their defenders’ caseloads as well as to provide them with resources such as expert witnesses and social workers. Funding is not found overnight, so in the meantime, the implementation of “participatory defense” can also help close the gap. The advantage of wealth is not found only in the courtroom but through nearly every part of the criminal justice system. From bail to parole, wealthier defendants typically see higher rates of success and lower rates of recidivism due to their ability to pay for these programs.
ContributorsAyd, Olivia (Author) / Koretz, Lora (Thesis director) / Moore, James (Committee member) / Dean, W.P. Carey School of Business (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Technological advancements have provided ease and accessibility for musicians to produce, publish, and share music worldwide. However, contradictory court rulings in determining what is protected under copyright law have developed an environment where top-of-the-chart and up-and-coming artists fear their records will be liable for copyright infringement. Throughout the twelve circuit courts in the United States, various legal tests are applied to copyright infringement cases. Most courts use two specific legal tests; the Second and Ninth Circuit court tests. This thesis analyzes how copyright law is applied to music, focusing on the ambiguous legal tests of the Second and Ninth Circuit Courts. This analysis aims to outline the flaws in the current legal tests and establish a new legal test dedicated to providing structure and uniformity to copyright law and music.
ContributorsSidi, Joshua (Author) / Koretz, Lora (Thesis director) / Moore, James (Committee member) / Barrett, The Honors College (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2022-05